Sunday, December 11, 2016

Why AI needs to drop the word ‘intelligence’

The Turing test arrived in the brilliant Computing Machinery & Intelligence
(1950), along with its nine defences, still an astounding paper that sets the
bar on whether machines can think and be intelligent. But it’s unfortunate that
the title includes the word ‘intelligence’ , as it is never mentioned in this
sense in the paper itself. It is also unfortunate that the phrase AI
(Artificial Intelligence) invented by John McCarthy in 1956 (the year of my
birth), at Dartmouth (where I studied), has become a misleading distraction.

Binet, who was responsible for inventing the IQ
(intelligence quotient) test, warned against it being seen as a sound measure
for individual intelligence or that it should be seen as ‘fixed’. His warnings
were not heeded as education itself became fixated with the search and
definition of a single measure of intelligence – IQ. The main protagonist being
Eysenck and it led to fraudulent policies, such as the 11+ in the UK, promoted on the back of fraudulent research by Cyril Burt.Stephen Jay Gould’s 1981 book The Mismeasure
of Man is only one of many that have criticised IQ research as narrow,
subject to reification (turns abstract concepts into concrete realities) and
linear ranking, when cognition is, in fact, a complex phenomenon. IQ research
has also been criticised for repeatedly confusing correlation with cause, not
only in heritability, where it is difficult to untangle nature from nurture,
but also when comparing scores in tests with future achievement. Class, culture
and gender may also play a role and the tests are not adjusted for these
variables.The focus on IQ, a search for a single, unitary
measure of the mind, is now seen by many as narrow and misleading. Most modern
theories of mind have moved on to more sophisticated views of the mind as with
different but interrelated cognitive abilities.Gardener tried to widen its definition into
Multiple Intelligences (1983) but this is weak science and lacks any real
vigour. It still suffers from a form of academic essentialism. More
importantly, it distorts the filed of what is known as Artificial Intelligence.

Drop word
‘intelligence’

We would do well to abandon the word ‘intelligence’, as it carries
with it so much bad theory and practice. Indeed AI has, in my view, already transcended
the term, as it gained competences across a much wider sets of competences (previously
intelligences), such as perception, translation, search, natural language
processing, speech, sentiment analysis, memory, retrieval and other many other domains.

Machine learning

Turing interestingly anticipated machine learning in AI, seeing
the computer as something that could be taught like a child. This complicated
the use of the word ‘intelligence’ further, as machines in this sense operate
dynamically in their environments, growing and gaining in competence. Machine
learning has led to successes all sorts of domains beyond the traditional field
of IQ and human ‘intelligences’. In many ways it is showing us the way, going
back to a wider set ofcompetences that
includes both ‘knowing that’ (cogntitive) and ‘knowing how’ (robotics) to do
things. This was seen by Turing as a real possibility and it frees us from the
fixed notion of intelligence that got so locked down into human genetics and
capabilities.

Human-all-too-human

Other formulations of capabilities may be found if
we do not focus on the anthropomorphic view of intelligence and learning but
rather competences. The word ‘intelligence’ has too much human import and
baggage. It makes man the measure of all things, whereas, it is clear that
computer power has already transcended our brain in some areas, in terms of
storage, exact recall, uploading, downloading, mathematical calculations,
chess, driving cars and so on.

The whole point of Google search, is not to be like us. It's better than us, with a greter memory, better serch and faster recall. Self-driving cars do not drive like us, they drive better than us. The whole point of the self-driving car is NOT to drive like us, as we kill 1.5 mllion people a yer while driving.

The great Turing did in fact explain that his
test was an attempt to transcend the religious idea of man as the measure of
all things but his test remains rooted in ’human-all-too-human’ abilities. Searle
(1980) rightly criticised Turing’s approach with his Chinese Room argument,
where the executor of the scripts in a translation task need know nothing at
all about the actual meaning of Chinese or English, yet pass the test i.e. they
do not ‘think’. Although this critique, in my view, makes a fundamental error.

Beyond human

Haugland (1997) questions the very idea that you need
meaningful understanding of the meaning of Chinese and English at all. Searle
seems to be demanding a human, gold-standard of understanding, self-awareness
and intelligence. If we free ourselves from the tyranny of human ‘intelligence’
to general problem solving, the problem is no longer a problem.

Let’s take this idea further. Koch (2014) claimed that ALL networks
are, to some degree ‘intelligent’. As the boundary for consciousness and
intelligence changed over time to include animals, indeed anything with a
network of neurons, he argues that intelligence is a property that can be
applied to any communicating network. As we have evidence that intelligence is
related to networked activity, whether these are brains or computers, could
intelligence be a function of this networking, so that all networked entities
are, to some degree, intelligent? Clark and Chalmers (1998) in The Extended Mind, laid out the
philosophical basis for this approach. This opens up the field for definitions
of ‘intelligence’ that are not benchmarked against human capabilities or speciesism.
If we consider the idea of competences residing in other forms of chemistry and
substrates, and see algorithms and their productive capabilities, as being
independent of the base materials in which they arise, then we can cut the ties
with the word ‘intelligence’ and focus on capabilities or competences.

Beyond brains

Wonderful as the brain may be, as the organ that named
itself and created all that we are discussing, it is a notoriously odd thing.
It takes over 20 years of solid educational instruction to turn it into a remotely
useful employee or member of society. It is famously inattentive, forgets most
of what you teach it (Ebbinghaus -1908), is sexist, racist, full of cognitive
baises (Kahneman -2011), sleeps 8 hours a day, can’t network, can’t upload, can’t
download and, here’s the fatal objection - it dies. This should not be the gold standard
for intelligence, as it is an idiosyncratic organ that evolved for
circumstances other than those we find ourselves in.

Beyond consciousness

We may even be able to move away from that other
anthropomorphic obsession - consciousness. Daniel Dennett (1995) in Consciousness
Explained, saw it as an epiphenomenon, not necessary for the explanation of
actual competence and action. If we can drop the ghost in the machine, the
machine itself can be seen as being capable, in a non-anthropomorphic sense.
Psychologically, Kahneman (2011) in Thinking Fast and Slow distinguishes between the deliberate,
slow rational and logical System 2 and the fast and instinctive System 1. He
links unconscious intuitions with conscious decision making but the most
interesting facet of his work is that we put too much faith in in human
intelligence and judgements. Incidentally, he sees both systems, as being full
of cognitive biases. Far from AI offering a world of bias it may be that AI can
free us from the world of human biases towards more objective problem solving.
The claim that all algorithms are biased, reduces statistics to a useless
slogan. Sure there may be bias, but the brain is intrinsically biased and
there’s little we can do about this.

If we move beyond brains, beyond inorganic versus inorganic,
we can move as Harari (2016) in Homo Deux recommends, towards intelligence
as fundamentally algorithmic and uncouple intelligence from humans and
consciousness. His argument is that natural selection itself is algorithmic and
gave rise to our species and brains, but these algorithms are independent of
the substances in which they reside. We must therefore readjust our thinking
around intelligence and learning to include wider definitions.

Beyond singular
intelligences

Networks, such as the internet, have provided a new
substrate, where collective intelligence is also possible, moving the concept
of intelligences and capabilities on further. One brain cannot download
directly from another or replicate knowledge and skills perfectly, in a
fraction of a second. This is already happening in AI. In ‘Cloud robotics’
robots learn from experience but can also learn from each other, as they are
networked. Experience and learning are therefore shared across the networked
robots. Researchers in Google have already been teaching robots to learn skills
in these three ways:

A. Learn motion skills (from direct experience)

B. Learn internal models (physics etc.)

C. Learn skills (human assisted)

In all three cases the learning is faster than one robot on
its own with more variation in the learning experiences. So deep learning
becomes not only possible from a mix of experience, models and being taught,
but also by being pooled.

This concept of pooled learning and competences is something
that drops the idea of a brain, human benchmark, or ‘subject’. Technology has
become largely networked, which avoids our all too human tendency to hang on to
‘essentialism’. When it comes to our human abilities and what we regard as
unique, we often invoke qualities such as ‘intuition’ or ‘thinking’ and
‘consciousness’. Turing opened up the possibility of “imaginable
digital computers“ that would perform astonishing feats of what we would
call intelligence or learning without recourse to a brain, soul or irreducible
quality such as consciousness. That is becoming a reality. AI is now
challenging what it is to be human, intelligent, competent, to think, to learn.
The challenges we face are not to mimic humans but to find solutions that we
are incapable of thinking of and executing. When Deepmind played its Atari
game, it shot round the edge of the blocks and attacked from above, something
humans had never thought of. We don’t want flawed human performance. We want
performance beyond our capabilities, that is better than human. Humans crash
cars and aircraft, kill patients through misdiagnosis and wrong prescribing. We
need technology that is better than us. We didn’t go faster by copying the legs
of a cheetah, we invented the wheel.

Beyond
cognitive computing

Turing clearly foresaw rapid advances in the power of
computers and, in the long term, was visionary in his understanding of their
potential capabilities. Remarkably, he successfully predicted that computers
would have 1Gig of storage by 2000. However, the Turing test itself has
been critiqued and is still a contentious area. He was wrong in assuming that “at
the end of the century the use of words and general educated opinion will have
altered so much that one will be able to speak of machines thinking without
expecting to be contradicted”. Many underestimated the recent advances in technology,
machine learning, reinforcement learning and deep learning, that have since
allowed AI to do the things that were regarded as unlikely, if not impossible.

Beyond IBMs misleading marketing

But the category mistake is to measure all of this in terms
of human performance. This is why IBM's Cognitive Computing is simply
misleading. There’s little that’s ‘Cognitive’ about it. That’s not to say that
Watson is not useful. I use it myself in WildFire. AI has become much more powerful, with
considerable advances in machine learning and in the practical application of
such advances. This is partly to do with advances in AI techniques but also
technical advances, which Turing predicted, and the rise of the internet and
massive data sets. Few dispute the impact of AI on tasks, not just in the
automation of manufacturing through robotics but also its impact in what has
been seen as the cognitive domain. We just don’t need the language of human
cognition to make progress - as that's a lie.

Conclusion

Few would argue that AI has progressed faster than expected,
with self-driving cars and significant advances in machine learning, deep
learning and reinforcement learning.In
some cases the practical applications clearly transcend human capabilities and
competences. We don’t need to see ‘intelligence’ at the centre of this solar
system. The Copernican move is to remove this term and replace it with
competences and look to problems that can be solved. The means to ends are
always means, it is the ends that matter. What is wonderful here is the opening
up of philosophical issues around agency, autonomy and morality. We are far
from the existential risk to our species that many foresee but there are many more
near-term issues to be considered. Ditching old psychological relics is one. Artificial smartness is with us it need not be called 'intelligent'.